End-to-End Supervised Product Quantization for Image Search and Retrieval
This work addresses the need for more accurate and efficient image retrieval systems, representing an incremental advancement by applying supervised learning to an existing unsupervised hashing technique.
The paper tackles the problem of improving image search and retrieval by introducing Deep Product Quantization (DPQ), a supervised dictionary-based hashing method that learns end-to-end, achieving state-of-the-art results in retrieval and classification with similar computational complexity as unsupervised Product Quantization.
Product Quantization, a dictionary based hashing method, is one of the leading unsupervised hashing techniques. While it ignores the labels, it harnesses the features to construct look up tables that can approximate the feature space. In recent years, several works have achieved state of the art results on hashing benchmarks by learning binary representations in a supervised manner. This work presents Deep Product Quantization (DPQ), a technique that leads to more accurate retrieval and classification than the latest state of the art methods, while having similar computational complexity and memory footprint as the Product Quantization method. To our knowledge, this is the first work to introduce a dictionary-based representation that is inspired by Product Quantization and which is learned end-to-end, and thus benefits from the supervised signal. DPQ explicitly learns soft and hard representations to enable an efficient and accurate asymmetric search, by using a straight-through estimator. Our method obtains state of the art results on an extensive array of retrieval and classification experiments.